A Fuzzy-Based Approach for the Diagnosis of Fault Modes in a Voltage-Fed PWM Inverter Induction Motor Drive

International audienceThis paper investigates the use of fuzzy logic for fault detection and diagnosis in a pulsewidth modulation voltage source inverter (PWM-VSI) induction motor drive. The proposed fuzzy technique requires the measurement of the output inverter currents to detect intermittent loss of firing pulses in the inverter power switches. For diagnosis purposes, a localization domain made with seven patterns is built with the stator Concordia current vector. One is dedicated to the healthy domain and the six others to each inverter power switch. The fuzzy bases of the proposed technique are extracted from the current analysis of the fault modes in the PWM-VSI. Experimental results on a 1.5-kW induction motor drive are presented to demonstrate the effectiveness of the proposed fuzzy approach

Fuzzy Pattern Recognition Based Fault Diagnosis

International audienceIn order to avoid catastrophic situations when the dynamics of a physical system (entity in Multi Agent System architecture) are evolving toward an undesirable operating mode, particular and quick safety actions have to be programmed in the control design. Classic control (PID and even state model based methods) becomes powerless for complex plants (nonlinear, MIMO and ill-defined systems). A more efficient diagnosis requires an artificial intelligence approach. We propose in this paper the design of a Fuzzy Pattern Recognition System (FPRS) that solves, in real time, the main following problems: 1) Identification of an actual state; 2) Identification of an eventual evolution towards a failure state; 3) Diagnosis and decision-making. Simulations have been carried for a fictive complex process plant with the objective to evaluate the consistency and the performance of the proposed diagnosis philosophy. The obtained results seem to be encouraging and very promising for application to fault diagnosis of a real and complex plant process

Online Condition Monitoring of Electric Powertrains using Machine Learning and Data Fusion

Safe and reliable operations of industrial machines are highly prioritized in industry. Typical industrial machines are complex systems, including electric motors, gearboxes and loads. A fault in critical industrial machines may lead to catastrophic failures, service interruptions and productivity losses, thus condition monitoring systems are necessary in such machines. The conventional condition monitoring or fault diagnosis systems using signal processing, time and frequency domain analysis of vibration or current signals are widely used in industry, requiring expensive and professional fault analysis team. Further, the traditional diagnosis methods mainly focus on single components in steady-state operations. Under dynamic operating conditions, the measured quantities are non-stationary, thus those methods cannot provide reliable diagnosis results for complex gearbox based powertrains, especially in multiple fault contexts. In this dissertation, four main research topics or problems in condition monitoring of gearboxes and powertrains have been identified, and novel solutions are provided based on data-driven approach. The first research problem focuses on bearing fault diagnosis at early stages and dynamic working conditions. The second problem is to increase the robustness of gearbox mixed fault diagnosis under noise conditions. Mixed fault diagnosis in variable speeds and loads has been considered as third problem. Finally, the limitation of labelled training or historical failure data in industry is identified as the main challenge for implementing data-driven algorithms. To address mentioned problems, this study aims to propose data-driven fault diagnosis schemes based on order tracking, unsupervised and supervised machine learning, and data fusion. All the proposed fault diagnosis schemes are tested with experimental data, and key features of the proposed solutions are highlighted with comparative studies.publishedVersio

Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems

[EN] Induction machines (IMs) power most modern industrial processes (induction motors) and generate an increasing portion of our electricity (doubly fed induction generators). A continuous monitoring of the machine's condition can identify faults at an early stage, and it can avoid costly, unexpected shutdowns of production processes, with economic losses well beyond the cost of the machine itself. Machine current signature analysis (MCSA), has become a prominent technique for condition-based maintenance, because, in its basic approach, it is non-invasive, requires just a current sensor, and can process the current signal using a standard fast Fourier transform (FFT). Nevertheless, the industrial application of MCSA requires well-trained maintenance personnel, able to interpret the current spectra and to avoid false diagnostics that can appear due to electrical noise in harsh industrial environments. This task faces increasing difficulties, especially when dealing with machines that work under non-stationary conditions, such as wind generators under variable wind regime, or motors fed from variable speed drives. In these cases, the resulting spectra are no longer simple one-dimensional plots in the time domain; instead, they become two-dimensional images in the joint time-frequency domain, requiring highly specialized personnel to evaluate the machine condition. To alleviate these problems, supporting the maintenance staff in their decision process, and simplifying the correct use of fault diagnosis systems, expert systems based on neural networks have been proposed for automatic fault diagnosis. However, all these systems, up to the best knowledge of the authors, operate under steady-state conditions, and are not applicable in a transient regime. To solve this problem, this paper presents an automatic system for generating optimized expert diagnostic systems for fault detection when the machine works under transient conditions. The proposed method is first theoretically introduced, and then it is applied to the experimental diagnosis of broken bars in a commercial cage induction motor.Burriel-Valencia, J.; Puche-Panadero, R.; Martinez-Roman, J.; Sapena-Bano, A.; Pineda-Sanchez, M.; Pérez-Cruz, J.; Riera-Guasp, M. (2019). Automatic Fault Diagnostic System for Induction Motors under Transient Regime Optimized with Expert Systems. Electronics. 8(1):1-16. https://doi.org/10.3390/electronics8010006S11681Puche-Panadero, R., Pineda-Sanchez, M., Riera-Guasp, M., Roger-Folch, J., Hurtado-Perez, E., & Perez-Cruz, J. (2009). Improved Resolution of the MCSA Method Via Hilbert Transform, Enabling the Diagnosis of Rotor Asymmetries at Very Low Slip. IEEE Transactions on Energy Conversion, 24(1), 52-59. doi:10.1109/tec.2008.2003207Abd-el -Malek, M., Abdelsalam, A. K., & Hassan, O. E. (2017). Induction motor broken rotor bar fault location detection through envelope analysis of start-up current using Hilbert transform. Mechanical Systems and Signal Processing, 93, 332-350. doi:10.1016/j.ymssp.2017.02.014Martinez, J., Belahcen, A., & Muetze, A. (2017). Analysis of the Vibration Magnitude of an Induction Motor With Different Numbers of Broken Bars. IEEE Transactions on Industry Applications, 53(3), 2711-2720. doi:10.1109/tia.2017.2657478Sapena-Bano, A., Pineda-Sanchez, M., Puche-Panadero, R., Perez-Cruz, J., Roger-Folch, J., Riera-Guasp, M., & Martinez-Roman, J. (2015). Harmonic Order Tracking Analysis: A Novel Method for Fault Diagnosis in Induction Machines. IEEE Transactions on Energy Conversion, 30(3), 833-841. doi:10.1109/tec.2015.2416973Sapena-Bano, A., Burriel-Valencia, J., Pineda-Sanchez, M., Puche-Panadero, R., & Riera-Guasp, M. (2017). The Harmonic Order Tracking Analysis Method for the Fault Diagnosis in Induction Motors Under Time-Varying Conditions. IEEE Transactions on Energy Conversion, 32(1), 244-256. doi:10.1109/tec.2016.2626008Burriel-Valencia, J., Puche-Panadero, R., Martinez-Roman, J., Sapena-Bano, A., & Pineda-Sanchez, M. (2017). Short-Frequency Fourier Transform for Fault Diagnosis of Induction Machines Working in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 66(3), 432-440. doi:10.1109/tim.2016.2647458Yin, Z., & Hou, J. (2016). Recent advances on SVM based fault diagnosis and process monitoring in complicated industrial processes. Neurocomputing, 174, 643-650. doi:10.1016/j.neucom.2015.09.081Bazan, G. H., Scalassara, P. R., Endo, W., Goedtel, A., Godoy, W. F., & Palácios, R. H. C. (2017). Stator fault analysis of three-phase induction motors using information measures and artificial neural networks. Electric Power Systems Research, 143, 347-356. doi:10.1016/j.epsr.2016.09.031Mustafidah, H., Hartati, S., Wardoyo, R., & Harjoko, A. (2014). Selection of Most Appropriate Backpropagation Training Algorithm in Data Pattern Recognition. International Journal of Computer Trends and Technology, 14(2), 92-95. doi:10.14445/22312803/ijctt-v14p120Godoy, W. F., da Silva, I. N., Lopes, T. D., Goedtel, A., & Palácios, R. H. C. (2016). Application of intelligent tools to detect and classify broken rotor bars in three-phase induction motors fed by an inverter. IET Electric Power Applications, 10(5), 430-439. doi:10.1049/iet-epa.2015.0469Ghorbanian, V., & Faiz, J. (2015). A survey on time and frequency characteristics of induction motors with broken rotor bars in line-start and inverter-fed modes. Mechanical Systems and Signal Processing, 54-55, 427-456. doi:10.1016/j.ymssp.2014.08.022Valles-Novo, R., de Jesus Rangel-Magdaleno, J., Ramirez-Cortes, J. M., Peregrina-Barreto, H., & Morales-Caporal, R. (2015). Empirical Mode Decomposition Analysis for Broken-Bar Detection on Squirrel Cage Induction Motors. IEEE Transactions on Instrumentation and Measurement, 64(5), 1118-1128. doi:10.1109/tim.2014.2373513De Santiago-Perez, J. J., Rivera-Guillen, J. R., Amezquita-Sanchez, J. P., Valtierra-Rodriguez, M., Romero-Troncoso, R. J., & Dominguez-Gonzalez, A. (2018). Fourier transform and image processing for automatic detection of broken rotor bars in induction motors. Measurement Science and Technology, 29(9), 095008. doi:10.1088/1361-6501/aad3aaMerabet, H., Bahi, T., Drici, D., Halam, N., & Bedoud, K. (2017). Diagnosis of rotor fault using neuro-fuzzy inference system. Journal of Fundamental and Applied Sciences, 9(1), 170. doi:10.4314/jfas.v9i1.12Riera-Guasp, M., Pineda-Sanchez, M., Perez-Cruz, J., Puche-Panadero, R., Roger-Folch, J., & Antonino-Daviu, J. A. (2012). Diagnosis of Induction Motor Faults via Gabor Analysis of the Current in Transient Regime. IEEE Transactions on Instrumentation and Measurement, 61(6), 1583-1596. doi:10.1109/tim.2012.2186650Gyftakis, K. N., Marques Cardoso, A. J., & Antonino-Daviu, J. A. (2017). Introducing the Filtered Park’s and Filtered Extended Park’s Vector Approach to detect broken rotor bars in induction motors independently from the rotor slots number. Mechanical Systems and Signal Processing, 93, 30-50. doi:10.1016/j.ymssp.2017.01.04

Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT

[EN] Recognition of characteristic patterns is proposed in this paper in order to diagnose the presence of electromechanical faults in induction electrical machines. Two common faults are considered; broken rotor bars and mixed eccentricities. The presence of these faults leads to the appearance of frequency components following a very characteristic evolution during the startup transient. The identification and extraction of these characteristic patterns through the Discrete Wavelet Transform (DWT) have been proven to be a reliable methodology for diagnosing the presence of these faults, showing certain advantages in comparison with the classical FFT analysis of the steady-state current. In the paper, a compilation of healthy and faulty cases are presented; they confirm the validity of the approach for the correct diagnosis of a wide range of electromechanical faults.The research leading to these results has received funding from the European Community's Seventh Framework Programme FP7/2007-2013 under Grant Agreement n° 224233 (Research Project PRODI “Power plant Robustification based on fault Detection and Isolation algorithms”). The authors also thank ‘Vicerrectorado de Investigación, Desarrollo e Innovación of Universidad Politécnica de Valencia’ for financing a part of this research through the program ‘Programa de Apoyo a la Investigación y Desarrollo (PAID-06-07).Antonino-Daviu, J.; Riera-Guasp, M.; Pineda-Sanchez, M.; Pons Llinares, J.; Puche-Panadero, R.; Pérez-Cruz, J. (2009). Feature Extraction for the Prognosis of Electromechanical Faults in Electrical Machines through the DWT. International Journal of Computational Intelligence Systems. 2(2):158-167. https://doi.org/10.2991/ijcis.2009.2.2.71581672

Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.Peer ReviewedPostprint (author's final draft